Data and Methods

With Lucan Way, Steven Levitsky & Yashar TalebiradPresented: EUI Democracy and Its Erosion Workshop 2026 (Florence) · APSA 2026 (Boston)

We present a framework for extracting structured autocratization events from human rights reports using keyword filtering and LLM-based extraction. The pipeline processes 9,677 country-year reports from the U.S. State Department, Freedom House, and Amnesty International (2000-2023) using an open-weight model with conservative, precision-first prompts and a two-threshold semantic deduplication layer. Applied at scale, it produces DERTT, a dataset of 76,792 unique autocratization incidents across 197 countries and 6 event types, each traceable to specific sentences in the source reports.

With Lucan Way & Steven LevitskyPresented: PSA 2026 (Oxford) · EPSS 2026 (Belfast)

How does resource concentration impact regime survival? On the one hand, the concentration of resources in government hands may allow dictators to weaken independent economic actors, bind large numbers to the state through the provision of attractive public employment, and facilitate efforts to impoverish, coopt, or divide organized opposition. On the other hand, high-level state involvement in the economy may render autocrats liable for a country’s economic woes and aggravate the economic loss of the incumbent elite during economic downturns, leading to higher rates of defection. To test these ideas, we created an original index of resource concentration that includes a variety of measures for the relative size of the government/private sector, state’s coercive control over the economy, and poverty and underdevelopment with a country-year-level dataset from 1970-2023. We show that higher levels of statist resource concentration significantly boost regime survival, controlled for the level of institutionalization. The findings have significant implications for understanding structural/political economy factors behind regime durability.

With Seva Gunitsky & Sahib Jafarov

Does the prosecution of former leaders by their own states strengthen or undermine democracy? Supporters of leader prosecutions emphasize their effects on accountability, transparency, and legal equality, while critics warn of elite vendettas, public disillusionment, and deepening polarization. Yet this debate has so far rested largely on normative or ideological claims rather than systematic evidence. To address this gap, this research note introduces the first comprehensive global dataset of leader prosecutions in hybrid and democratic regimes from 1989 to 2023. We find that the practice is surprisingly common -- over a quarter of all non-autocratic leaders have been prosecuted by their own governments during this period. We find no evidence that poorer or less institutionally capable countries are more likely to prosecute, and little evidence that prosecutions are followed by cycles of political retribution. We also find evidence of regional contagion, especially in recent years. These findings, while suggestive, challenge the idea that prosecutions are markers of democratic decline, instead highlighting legal capacity and political context as crucial factors in the decision to prosecute.

With Seva Gunitsky & Sahib Jafarov

Proponents of these prosecutions argue that they strengthen democracy by reinforcing accountability, while opponents counter that they erode trust and drive polarization. But what effect do these prosecutions actually have? To answer this question, we have created a comprehensive new dataset of modern leaders who have been prosecuted by their own governments.

With Amir Abdul Reda & Mohamed AbdallaPresented: APSA 2019 (DC)

How can we measure the resource mobilization (RM) efforts of social movements on Twitter? In this article, we create the first-ever measure of social movements’ RM efforts on a social media platform. To this aim, we create a four-conditional lexicon that can parse through tweets and identify those concerned with RM. We also create a simple RM score that can be plotted in a time series format to track the RM efforts of social movements in real-time. We use our tools with millions of tweets from the United States streamed between November 28, 2018, and February 11, 2019, to demonstrate how our measure can help us estimate the saliency and persistency of social movements’ RM efforts. We find that our measure captures RM by successfully cross-checking the variation of this score against protest events in the United States during the same time frame. Finally, we illustrate the descriptive and qualitative utility of our tools for understanding social movements by running conventional topic modeling algorithms on the tweets that were used to compute the RM score and point at specific avenues for theory building and testing.